Energy Ring: Stabilizing Feature Energy for Reliable Test-Time Discovery

Abstract:Test-Time Discovery (TTD) aims to recognize known classes and detect novel ones while the model is already deployed, operating directly on a continual unlabeled test-time stream. Existing TTD methods are overly conservative, making known and unknown features collapse together and difficult to separate during test-time adaptation. In this paper, we revisit TTD from an energy perspective and find that the natural energy gap between known and unknown classes is unstable and easily collapses. We therefore propose Energy Ring to stabilize this gap by anchoring the two types of classes on separate energy rings. A lightweight adapter enlarges the norm-based gap while preserving known-class directions, and a perturbation consistency module forms compact clusters for unknown classes. During testing, energy-guided confidence weighting improves prediction, and a stability-based rectification mechanism corrects unreliable updates. Experimental results show that Energy Ring significantly improves TTD performance against existing TTD methods.

Artificial Intelligence (cs.AI)

arXiv:2610.14525 [cs.AI]

(or arXiv:2610.14525v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.14525 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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